A social navigation system collects data from its users—its community—about what they are doing, their opinions, and their decisions, aggregates this data, and provides the aggregated data—community data—back to individuals so that they can use it to guide behavior and decisions. Social navigation systems empower users with the ability to leverage social information on a much larger and faster scale than they can in the physical world. With social navigation systems, users can “see” what many, many other people have done without directly interacting with or observing them and can do so at a time when it is most beneficial to them.

The popularity of social navigation systems indicates that both designers and users perceive value in them, but evaluations of social navigation systems yield surprising and mixed results. These findings suggest that while social navigation systems can lead users to good decisions and outcomes, they can also lead users to unexpected and potentially undesirable decisions and outcomes. In this thesis, I document my investigation of the user experience for social navigation systems that employ activity data. I define the social navigation user experience to be how users perceive, make sense of, and employ community data from social navigation systems.

I make three main contributions in this thesis. First, I synthesize social navigation systems research with research in social influence, advice-taking, and informational cascades to construct hypotheses about the social navigation user experience. These hypotheses posit that community data from a social navigation system exerts informational influence on users, that users egocentrically discount community data, that herding in social navigation systems can be characterized as informational cascades, and that the size and unanimity of the community data correspond to the strength of the community data’s influence.

The second contribution of this thesis is an experiment that evaluates the hypotheses about the social navigation user experience; this experiment investigated how a social navigation system can support online charitable giving decisions. The experiment’s results support the majority of the hypotheses about the social navigation user experience and provide mixed evidence for the other hypotheses. The results show that the social navigation system’s community data exerted informational influence on participants and that the herding in social navigation systems can be characterized as informational cascades. The results suggest that participants egocentrically discounted community data; however, because the experiment was not designed to directly measure egocentric discounting, it is not possible to verify this hypothesis. The experiment’s results show that the unanimity of the community data is a significant factor in the effect that the community data has on participants’ decisions, but that the size of the community data was only significant in some instances. Finally, the results indicate that participants were skeptical of making a donation in general, and the community data was much more influential when reinforcing this skepticism as compared to overcoming it.

The implications that arise from the experiment’s findings compromise the final contribution of this thesis. Broadly, these implications concern improving the design of social navigation systems and developing a general framework for evaluating the social influence of social navigation systems. The approach to improving social navigation systems is grounded in the development of methods to capture, aggregate, and represents objective information rather than actions or decisions. A general framework for evaluating the social influence of social navigation systems derives from the experimental design of the nonprofit choice experiment; this framework standardizes the inputs, outputs, and analyzes for social navigation systems. The benefits of this framework include comparing social navigation systems within and across domains and comparing results from evaluations of social navigation experiments to results from experiments in social influence and informational cascades.